In [ ]:
###############################################################
# Start to connecting site Tesacom by API                        
# collecting raw data 
# Transform data to JSON model and convert Dataframe 
# Date = 4/08/2021   Programmer : Marco Inda
###############################################################
In [22]:
import requests
from pandas import json_normalize
import json
import pandas as pd
import sys
import seaborn as sns
import matplotlib.pyplot as plt
%matplotlib inline
%config InlineBackend.figure_format = 'svg'

url = 'https://vdms.tesacom.net/api/reports'
#url = 'https://vdms.tesacom.net/api/reports?positionId=0'
#params = dict(apikey='H94pPRmHEZwUhdrJH7W4EtZS', start='2021-02-03T00:00:00', end='2021-02-03T23:59:59',assetname='E060522')
params = dict(apikey='H94pPRmHEZwUhdrJH7W4EtZS',start='2021-07-01T00:00:00',end='2021-07-31T23:59:59',assetname='E030214')     

res = requests.get(url,params=params)

if res.status_code != 200:
    # This means something went wrong.
    raise ApiError('GET /tasks/ {}'.format(res.status_code))
# otra forma de rescatar a json los datos
json_data =res.json()
print (json_data)
#json_normalize(json_data)
# El json lo convierto a DataFrame
bn =pd.DataFrame(json_data)
#bn.head(10)

[{'id_posicion': '109635488', 'fecha': '2021/07/31 11:56:22', 'title': 'E030214', 'id_flota': '248', 'lat': -34.695858, 'lng': -58.55118, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-31T14:56:22.0000000 00:00', 'Latitude': -34.695858, 'Longitude': -58.55118}, 'CumulativeOperatingHours': {'datetime': '2021-07-31T14:56:22.0000000 00:00', 'Hour': 2020.9}, 'EngineOilPressure': {'Pressure': 272, 'datetime': '2021-07-31T14:56:22.0000000 00:00', 'pressure': 272, 'UnitOfMeasure': 'kPa'}, 'EngineCoolantTemp': {'datetime': '2021-07-31T14:56:22.0000000 00:00', 'Temperature': 85, 'UnitOfMeasure': 'C'}, 'BatteryVoltage': {'datetime': '2021-07-31T14:56:22.0000000 00:00', 'Voltage': 12.5}, 'EngineLoad': {'datetime': '2021-07-31T14:56:22.0000000 00:00', 'Percent': 27}, 'EngineSpeed': {'Speed': 1798, 'datetime': '2021-07-31T14:56:22.0000000 00:00', 'speed': 1798, 'UnitOfMeasure': 'RPM'}}}}, {'id_posicion': '109630078', 'fecha': '2021/07/31 10:56:21', 'title': 'E030214', 'id_flota': '248', 'lat': -34.696087, 'lng': -58.55127, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-31T13:56:21.0000000 00:00', 'Latitude': -34.696087, 'Longitude': -58.55127}, 'CumulativeOperatingHours': {'datetime': '2021-07-31T13:56:21.0000000 00:00', 'Hour': 2019.9}, 'EngineOilPressure': {'Pressure': 4, 'datetime': '2021-07-31T13:56:21.0000000 00:00', 'pressure': 4, 'UnitOfMeasure': 'kPa'}, 'EngineCoolantTemp': {'datetime': '2021-07-31T13:56:21.0000000 00:00', 'Temperature': 66, 'UnitOfMeasure': 'C'}, 'BatteryVoltage': {'datetime': '2021-07-31T13:56:21.0000000 00:00', 'Voltage': 12.5}, 'EngineLoad': {'datetime': '2021-07-31T13:56:21.0000000 00:00', 'Percent': 0}, 'EngineSpeed': {'Speed': 0, 'datetime': '2021-07-31T13:56:21.0000000 00:00', 'speed': 0, 'UnitOfMeasure': 'RPM'}}}}, {'id_posicion': '109630077', 'fecha': '2021/07/31 10:20:24', 'title': 'E030214', 'id_flota': '248', 'lat': -34.6959, 'lng': -58.551174, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-31T13:20:24.0000000 00:00', 'Latitude': -34.6959, 'Longitude': -58.551174}, 'CumulativeOperatingHours': {'datetime': '2021-07-31T13:20:24.0000000 00:00', 'Hour': 2019.9}, 'EngineOilPressure': {'Pressure': 232, 'datetime': '2021-07-31T13:20:24.0000000 00:00', 'pressure': 232, 'UnitOfMeasure': 'kPa'}, 'EngineCoolantTemp': {'datetime': '2021-07-31T13:20:24.0000000 00:00', 'Temperature': 86, 'UnitOfMeasure': 'C'}, 'BatteryVoltage': {'datetime': '2021-07-31T13:20:24.0000000 00:00', 'Voltage': 12.95}, 'EngineLoad': {'datetime': '2021-07-31T13:20:24.0000000 00:00', 'Percent': 11}, 'EngineSpeed': {'Speed': 1196.25, 'datetime': '2021-07-31T13:20:24.0000000 00:00', 'speed': 1196.25, 'UnitOfMeasure': 'RPM'}}}}, {'id_posicion': '109630076', 'fecha': '2021/07/31 10:08:48', 'title': 'E030214', 'id_flota': '248', 'lat': -34.695858, 'lng': -58.551155, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-31T13:08:48.0000000 00:00', 'Latitude': -34.695858, 'Longitude': -58.551155}, 'CumulativeOperatingHours': {'datetime': '2021-07-31T13:08:48.0000000 00:00', 'Hour': 2019.75}, 'EngineOilPressure': {'Pressure': 272, 'datetime': '2021-07-31T13:08:48.0000000 00:00', 'pressure': 272, 'UnitOfMeasure': 'kPa'}, 'EngineCoolantTemp': {'datetime': '2021-07-31T13:08:48.0000000 00:00', 'Temperature': 86, 'UnitOfMeasure': 'C'}, 'BatteryVoltage': {'datetime': '2021-07-31T13:08:48.0000000 00:00', 'Voltage': 13}, 'EngineLoad': {'datetime': '2021-07-31T13:08:48.0000000 00:00', 'Percent': 14}, 'EngineSpeed': {'Speed': 1799.12, 'datetime': '2021-07-31T13:08:48.0000000 00:00', 'speed': 1799.12, 'UnitOfMeasure': 'RPM'}}}}, {'id_posicion': '109625064', 'fecha': '2021/07/31 09:08:48', 'title': 'E030214', 'id_flota': '248', 'lat': -34.69572, 'lng': -58.551353, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-31T12:08:48.0000000 00:00', 'Latitude': -34.69572, 'Longitude': -58.551353}, 'CumulativeOperatingHours': {'datetime': '2021-07-31T12:08:48.0000000 00:00', 'Hour': 2018.75}, 'EngineOilPressure': {'Pressure': 228, 'datetime': '2021-07-31T12:08:48.0000000 00:00', 'pressure': 228, 'UnitOfMeasure': 'kPa'}, 'EngineCoolantTemp': {'datetime': '2021-07-31T12:08:48.0000000 00:00', 'Temperature': 85, 'UnitOfMeasure': 'C'}, 'BatteryVoltage': {'datetime': '2021-07-31T12:08:48.0000000 00:00', 'Voltage': 13}, 'EngineLoad': {'datetime': '2021-07-31T12:08:48.0000000 00:00', 'Percent': 11}, 'EngineSpeed': {'Speed': 1198.38, 'datetime': '2021-07-31T12:08:48.0000000 00:00', 'speed': 1198.38, 'UnitOfMeasure': 'RPM'}}}}, {'id_posicion': '109620370', 'fecha': '2021/07/31 08:09:01', 'title': 'E030214', 'id_flota': '248', 'lat': -34.695904, 'lng': -58.551548, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-31T11:09:01.0000000 00:00', 'Latitude': -34.695904, 'Longitude': -58.551548}}}}, {'id_posicion': '109620369', 'fecha': '2021/07/31 08:08:47', 'title': 'E030214', 'id_flota': '248', 'lat': -34.695904, 'lng': -58.551548, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-31T11:08:47.0000000 00:00', 'Latitude': -34.695904, 'Longitude': -58.551548}, 'CumulativeOperatingHours': {'datetime': '2021-07-31T11:08:47.0000000 00:00', 'Hour': 2017.75}, 'EngineOilPressure': {'Pressure': 0, 'datetime': '2021-07-31T11:08:47.0000000 00:00', 'pressure': 0, 'UnitOfMeasure': 'kPa'}, 'EngineCoolantTemp': {'datetime': '2021-07-31T11:08:47.0000000 00:00', 'Temperature': 10, 'UnitOfMeasure': 'C'}, 'BatteryVoltage': {'datetime': '2021-07-31T11:08:47.0000000 00:00', 'Voltage': 11.2}, 'EngineLoad': {'datetime': '2021-07-31T11:08:47.0000000 00:00', 'Percent': 0}, 'EngineSpeed': {'Speed': 0, 'datetime': '2021-07-31T11:08:47.0000000 00:00', 'speed': 0, 'UnitOfMeasure': 'RPM'}}}}, {'id_posicion': '109620368', 'fecha': '2021/07/31 08:08:42', 'title': 'E030214', 'id_flota': '248', 'lat': -34.695904, 'lng': -58.551548, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-31T11:08:42.0000000 00:00', 'Latitude': -34.695904, 'Longitude': -58.551548}}}}, {'id_posicion': '109598774', 'fecha': '2021/07/31 03:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.69588, 'lng': -58.551277, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-31T06:00:00.0000000 00:00', 'Latitude': -34.69588, 'Longitude': -58.551277}}}}, {'id_posicion': '109573627', 'fecha': '2021/07/30 21:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.69588, 'lng': -58.551277, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-31T00:00:00.0000000 00:00', 'Latitude': -34.69588, 'Longitude': -58.551277}}}}, {'id_posicion': '109550208', 'fecha': '2021/07/30 16:05:38', 'title': 'E030214', 'id_flota': '248', 'lat': -34.69595, 'lng': -58.55118, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-30T19:05:38.0000000 00:00', 'Latitude': -34.69595, 'Longitude': -58.55118}, 'CumulativeOperatingHours': {'datetime': '2021-07-30T19:05:38.0000000 00:00', 'Hour': 2017.7}, 'EngineOilPressure': {'Pressure': 256, 'datetime': '2021-07-30T19:05:38.0000000 00:00', 'pressure': 256, 'UnitOfMeasure': 'kPa'}, 'EngineCoolantTemp': {'datetime': '2021-07-30T19:05:38.0000000 00:00', 'Temperature': 87, 'UnitOfMeasure': 'C'}, 'BatteryVoltage': {'datetime': '2021-07-30T19:05:38.0000000 00:00', 'Voltage': 12.85}, 'EngineLoad': {'datetime': '2021-07-30T19:05:38.0000000 00:00', 'Percent': 0}, 'EngineSpeed': {'Speed': 1325, 'datetime': '2021-07-30T19:05:38.0000000 00:00', 'speed': 1325, 'UnitOfMeasure': 'RPM'}}}}, {'id_posicion': '109545668', 'fecha': '2021/07/30 15:37:48', 'title': 'E030214', 'id_flota': '248', 'lat': -34.695942, 'lng': -58.551277, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-30T18:37:48.0000000 00:00', 'Latitude': -34.695942, 'Longitude': -58.551277}, 'CumulativeOperatingHours': {'datetime': '2021-07-30T18:37:48.0000000 00:00', 'Hour': 2017.25}, 'EngineOilPressure': {'Pressure': 272, 'datetime': '2021-07-30T18:37:48.0000000 00:00', 'pressure': 272, 'UnitOfMeasure': 'kPa'}, 'EngineCoolantTemp': {'datetime': '2021-07-30T18:37:48.0000000 00:00', 'Temperature': 86, 'UnitOfMeasure': 'C'}, 'BatteryVoltage': {'datetime': '2021-07-30T18:37:48.0000000 00:00', 'Voltage': 12.65}, 'EngineLoad': {'datetime': '2021-07-30T18:37:48.0000000 00:00', 'Percent': 17}, 'EngineSpeed': {'Speed': 1801.38, 'datetime': '2021-07-30T18:37:48.0000000 00:00', 'speed': 1801.38, 'UnitOfMeasure': 'RPM'}}}}, {'id_posicion': '109540385', 'fecha': '2021/07/30 14:37:47', 'title': 'E030214', 'id_flota': '248', 'lat': -34.695957, 'lng': -58.551155, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-30T17:37:47.0000000 00:00', 'Latitude': -34.695957, 'Longitude': -58.551155}, 'CumulativeOperatingHours': {'datetime': '2021-07-30T17:37:47.0000000 00:00', 'Hour': 2016.25}, 'EngineOilPressure': {'Pressure': 280, 'datetime': '2021-07-30T17:37:47.0000000 00:00', 'pressure': 280, 'UnitOfMeasure': 'kPa'}, 'EngineCoolantTemp': {'datetime': '2021-07-30T17:37:47.0000000 00:00', 'Temperature': 73, 'UnitOfMeasure': 'C'}, 'BatteryVoltage': {'datetime': '2021-07-30T17:37:47.0000000 00:00', 'Voltage': 12.2}, 'EngineLoad': {'datetime': '2021-07-30T17:37:47.0000000 00:00', 'Percent': 17}, 'EngineSpeed': {'Speed': 1820.62, 'datetime': '2021-07-30T17:37:47.0000000 00:00', 'speed': 1820.62, 'UnitOfMeasure': 'RPM'}}}}, {'id_posicion': '109540384', 'fecha': '2021/07/30 14:18:50', 'title': 'E030214', 'id_flota': '248', 'lat': -34.695957, 'lng': -58.551155, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-30T17:18:50.0000000 00:00', 'Latitude': -34.695957, 'Longitude': -58.551155}, 'CumulativeOperatingHours': {'datetime': '2021-07-30T17:18:50.0000000 00:00', 'Hour': 2016.25}, 'EngineOilPressure': {'Pressure': 236, 'datetime': '2021-07-30T17:18:50.0000000 00:00', 'pressure': 236, 'UnitOfMeasure': 'kPa'}, 'EngineCoolantTemp': {'datetime': '2021-07-30T17:18:50.0000000 00:00', 'Temperature': 86, 'UnitOfMeasure': 'C'}, 'BatteryVoltage': {'datetime': '2021-07-30T17:18:50.0000000 00:00', 'Voltage': 12.85}, 'EngineLoad': {'datetime': '2021-07-30T17:18:50.0000000 00:00', 'Percent': 12}, 'EngineSpeed': {'Speed': 1199, 'datetime': '2021-07-30T17:18:50.0000000 00:00', 'speed': 1199, 'UnitOfMeasure': 'RPM'}}}}, {'id_posicion': '109536009', 'fecha': '2021/07/30 13:29:43', 'title': 'E030214', 'id_flota': '248', 'lat': -34.695946, 'lng': -58.551155, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-30T16:29:43.0000000 00:00', 'Latitude': -34.695946, 'Longitude': -58.551155}, 'CumulativeOperatingHours': {'datetime': '2021-07-30T16:29:43.0000000 00:00', 'Hour': 2015.45}, 'EngineOilPressure': {'Pressure': 256, 'datetime': '2021-07-30T16:29:43.0000000 00:00', 'pressure': 256, 'UnitOfMeasure': 'kPa'}, 'EngineCoolantTemp': {'datetime': '2021-07-30T16:29:43.0000000 00:00', 'Temperature': 64, 'UnitOfMeasure': 'C'}, 'BatteryVoltage': {'datetime': '2021-07-30T16:29:43.0000000 00:00', 'Voltage': 12.55}, 'EngineLoad': {'datetime': '2021-07-30T16:29:43.0000000 00:00', 'Percent': 13}, 'EngineSpeed': {'Speed': 1405.88, 'datetime': '2021-07-30T16:29:43.0000000 00:00', 'speed': 1405.88, 'UnitOfMeasure': 'RPM'}}}}, {'id_posicion': '109530497', 'fecha': '2021/07/30 12:42:23', 'title': 'E030214', 'id_flota': '248', 'lat': -34.696022, 'lng': -58.551277, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-30T15:42:23.0000000 00:00', 'Latitude': -34.696022, 'Longitude': -58.551277}, 'CumulativeOperatingHours': {'datetime': '2021-07-30T15:42:23.0000000 00:00', 'Hour': 2015.45}, 'EngineOilPressure': {'Pressure': 248, 'datetime': '2021-07-30T15:42:23.0000000 00:00', 'pressure': 248, 'UnitOfMeasure': 'kPa'}, 'EngineCoolantTemp': {'datetime': '2021-07-30T15:42:23.0000000 00:00', 'Temperature': 85, 'UnitOfMeasure': 'C'}, 'BatteryVoltage': {'datetime': '2021-07-30T15:42:23.0000000 00:00', 'Voltage': 12.7}, 'EngineLoad': {'datetime': '2021-07-30T15:42:23.0000000 00:00', 'Percent': 8}, 'EngineSpeed': {'Speed': 1295.12, 'datetime': '2021-07-30T15:42:23.0000000 00:00', 'speed': 1295.12, 'UnitOfMeasure': 'RPM'}}}}, {'id_posicion': '109530496', 'fecha': '2021/07/30 12:38:12', 'title': 'E030214', 'id_flota': '248', 'lat': -34.695877, 'lng': -58.55122, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-30T15:38:12.0000000 00:00', 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'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-07T00:00:00.0000000 00:00', 'Latitude': -34.656876, 'Longitude': -58.379135}}}}, {'id_posicion': '106874042', 'fecha': '2021/07/06 15:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.656826, 'lng': -58.379154, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-06T18:00:00.0000000 00:00', 'Latitude': -34.656826, 'Longitude': -58.379154}}}}, {'id_posicion': '106840379', 'fecha': '2021/07/06 09:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.656826, 'lng': -58.379154, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-06T12:00:00.0000000 00:00', 'Latitude': -34.656826, 'Longitude': -58.379154}}}}, {'id_posicion': '106814189', 'fecha': '2021/07/06 03:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.656765, 'lng': -58.378994, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-06T06:00:00.0000000 00:00', 'Latitude': -34.656765, 'Longitude': -58.378994}}}}, {'id_posicion': '106790458', 'fecha': '2021/07/05 21:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.656776, 'lng': -58.379047, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-06T00:00:00.0000000 00:00', 'Latitude': -34.656776, 'Longitude': -58.379047}}}}, {'id_posicion': '106765089', 'fecha': '2021/07/05 15:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.65683, 'lng': -58.379093, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-05T18:00:00.0000000 00:00', 'Latitude': -34.65683, 'Longitude': -58.379093}}}}, {'id_posicion': '106734834', 'fecha': '2021/07/05 09:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.65683, 'lng': -58.379093, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-05T12:00:00.0000000 00:00', 'Latitude': -34.65683, 'Longitude': -58.379093}}}}, {'id_posicion': '106709187', 'fecha': '2021/07/05 03:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.65677, 'lng': -58.379456, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-05T06:00:00.0000000 00:00', 'Latitude': -34.65677, 'Longitude': -58.379456}}}}, {'id_posicion': '106683734', 'fecha': '2021/07/04 21:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.65677, 'lng': -58.379456, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-05T00:00:00.0000000 00:00', 'Latitude': -34.65677, 'Longitude': -58.379456}}}}, {'id_posicion': '106658557', 'fecha': '2021/07/04 15:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.656895, 'lng': -58.379227, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-04T18:00:00.0000000 00:00', 'Latitude': -34.656895, 'Longitude': -58.379227}}}}, {'id_posicion': '106635360', 'fecha': '2021/07/04 09:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.656895, 'lng': -58.379227, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-04T12:00:00.0000000 00:00', 'Latitude': -34.656895, 'Longitude': -58.379227}}}}, {'id_posicion': '106611845', 'fecha': '2021/07/04 03:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.656834, 'lng': -58.37901, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-04T06:00:00.0000000 00:00', 'Latitude': -34.656834, 'Longitude': -58.37901}}}}, {'id_posicion': '106589246', 'fecha': '2021/07/03 21:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.656834, 'lng': -58.37901, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-04T00:00:00.0000000 00:00', 'Latitude': -34.656834, 'Longitude': -58.37901}}}}, {'id_posicion': '106564709', 'fecha': '2021/07/03 15:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.657085, 'lng': -58.379433, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-03T18:00:00.0000000 00:00', 'Latitude': -34.657085, 'Longitude': -58.379433}}}}, {'id_posicion': '106536933', 'fecha': '2021/07/03 09:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.657085, 'lng': -58.379433, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-03T12:00:00.0000000 00:00', 'Latitude': -34.657085, 'Longitude': -58.379433}}}}, {'id_posicion': '106511148', 'fecha': '2021/07/03 03:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.65689, 'lng': -58.379227, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-03T06:00:00.0000000 00:00', 'Latitude': -34.65689, 'Longitude': -58.379227}}}}, {'id_posicion': '106470987', 'fecha': '2021/07/02 21:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.65693, 'lng': -58.379154, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-03T00:00:00.0000000 00:00', 'Latitude': -34.65693, 'Longitude': -58.379154}}}}, {'id_posicion': '106444927', 'fecha': '2021/07/02 15:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.65693, 'lng': -58.379154, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-02T18:00:00.0000000 00:00', 'Latitude': -34.65693, 'Longitude': -58.379154}}}}, {'id_posicion': '106413188', 'fecha': '2021/07/02 09:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.656967, 'lng': -58.378983, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-02T12:00:00.0000000 00:00', 'Latitude': -34.656967, 'Longitude': -58.378983}}}}, {'id_posicion': '106385650', 'fecha': '2021/07/02 03:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.65688, 'lng': -58.37915, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-02T06:00:00.0000000 00:00', 'Latitude': -34.65688, 'Longitude': -58.37915}}}}, {'id_posicion': '106363232', 'fecha': '2021/07/01 21:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.65682, 'lng': -58.379093, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-02T00:00:00.0000000 00:00', 'Latitude': -34.65682, 'Longitude': -58.379093}}}}, {'id_posicion': '106338669', 'fecha': '2021/07/01 15:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.65682, 'lng': -58.379093, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-01T18:00:00.0000000 00:00', 'Latitude': -34.65682, 'Longitude': -58.379093}}}}, {'id_posicion': '106306384', 'fecha': '2021/07/01 09:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.656796, 'lng': -58.379086, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-01T12:00:00.0000000 00:00', 'Latitude': -34.656796, 'Longitude': -58.379086}}}}, {'id_posicion': '106277093', 'fecha': '2021/07/01 03:00:00', 'title': 'E030214', 'id_flota': '248', 'lat': -34.656765, 'lng': -58.379055, 'data': {'flota': 'Bs.As.', 'patente': '0300196066', 't_vehiculo': '35', 'velocidad': '0.000', 'rumbo': '0', 'altitud': '0', 'evento': 'Posicion normal', 'color': '#39b80f', 'telemetry': {'EquipmentHeader': {'UnitInstallDateTime': '2021-01-05T14:27:20.0000000 00:00', 'OEMName': 'JLG', 'Model': '1350SJP', 'EquipmentID': 'E030214', 'SerialNumber': '0300196066'}, 'Location': {'datetime': '2021-07-01T06:00:00.0000000 00:00', 'Latitude': -34.656765, 'Longitude': -58.379055}}}}]
In [ ]:
#####################################################################################
#      Finish to Collect Data  
#
#####################################################################################
In [24]:
######################################################################################
#  Collecting data about six Month from files .csv for one machine: JLG 1350 - E030214   
#  transform date in flat file Dataframe python
#  review statisticals about samples
######################################################################################
import pandas as pd 
import dask.dataframe as dd

dfs = []
dfs.append(pd.read_csv(r'C:\Users\minda\backup\E030214_01_02.csv', header=0, parse_dates=[3], index_col=3, squeeze=True))
dfs.append(pd.read_csv(r'C:\Users\minda\backup\E030214_03.csv', header=0, parse_dates=[3], index_col=3, squeeze=True))
dfs.append(pd.read_csv(r'C:\Users\minda\backup\E030214_04.csv', header=0, parse_dates=[3], index_col=3, squeeze=True))
dfs.append(pd.read_csv(r'C:\Users\minda\backup\E030214_05.csv', header=0, parse_dates=[3], index_col=3, squeeze=True))
dfs.append(pd.read_csv(r'C:\Users\minda\backup\E030214_06.csv', header=0, parse_dates=[3], index_col=3, squeeze=True))
bn = pd.concat(dfs,ignore_index= False )
bn.head(20)
Out[24]:
Unnamed: 0 index id_posicion title id_flota lat lng data.flota data.patente data.t_vehiculo ... data.telemetry.EngineCoolantTemp.Temperature data.telemetry.EngineCoolantTemp.UnitOfMeasure data.telemetry.BatteryVoltage.datetime data.telemetry.BatteryVoltage.Voltage data.telemetry.EngineLoad.datetime data.telemetry.EngineLoad.Percent data.telemetry.EngineSpeed.Speed data.telemetry.EngineSpeed.datetime data.telemetry.EngineSpeed.speed data.telemetry.EngineSpeed.UnitOfMeasure
fecha
2021-01-14 14:37:44 1067 1067 90006767 E030214 248 -33.359980 -60.149075 Bs.As. 300196066 35 ... 85.0 C 2021-01-14T17:37:44.0000000 00:00 12.70 2021-01-14T17:37:44.0000000 00:00 10.0 1199.88 2021-01-14T17:37:44.0000000 00:00 1199.88 RPM
2021-01-14 15:37:44 1066 1066 90006876 E030214 248 -33.359890 -60.149094 Bs.As. 300196066 35 ... 86.0 C 2021-01-14T18:37:44.0000000 00:00 12.70 2021-01-14T18:37:44.0000000 00:00 10.0 1199.25 2021-01-14T18:37:44.0000000 00:00 1199.25 RPM
2021-01-14 16:37:44 1065 1065 90009511 E030214 248 -33.359947 -60.149140 Bs.As. 300196066 35 ... 86.0 C 2021-01-14T19:37:44.0000000 00:00 12.05 2021-01-14T19:37:44.0000000 00:00 37.0 1790.88 2021-01-14T19:37:44.0000000 00:00 1790.88 RPM
2021-01-14 16:49:57 1064 1064 90009512 E030214 248 -33.359890 -60.149185 Bs.As. 300196066 35 ... 87.0 C 2021-01-14T19:49:57.0000000 00:00 12.70 2021-01-14T19:49:57.0000000 00:00 10.0 1204.88 2021-01-14T19:49:57.0000000 00:00 1204.88 RPM
2021-01-14 16:50:48 1063 1063 90009513 E030214 248 -33.359962 -60.148910 Bs.As. 300196066 35 ... 81.0 C 2021-01-14T19:50:48.0000000 00:00 12.10 2021-01-14T19:50:48.0000000 00:00 13.0 1417.62 2021-01-14T19:50:48.0000000 00:00 1417.62 RPM
2021-01-14 16:51:16 1062 1062 90009514 E030214 248 -33.359962 -60.148910 Bs.As. 300196066 35 ... 87.0 C 2021-01-14T19:51:16.0000000 00:00 12.55 2021-01-14T19:51:16.0000000 00:00 10.0 1217.00 2021-01-14T19:51:16.0000000 00:00 1217.00 RPM
2021-01-14 19:46:08 1059 1059 90017717 E030214 248 -33.359730 -60.149017 Bs.As. 300196066 35 ... 47.0 C 2021-01-14T22:46:08.0000000 00:00 12.25 2021-01-14T22:46:08.0000000 00:00 26.0 1799.50 2021-01-14T22:46:08.0000000 00:00 1799.50 RPM
2021-01-14 20:02:57 1058 1058 90020188 E030214 248 -33.359940 -60.149384 Bs.As. 300196066 35 ... 85.0 C 2021-01-14T23:02:57.0000000 00:00 12.65 2021-01-14T23:02:57.0000000 00:00 12.0 1213.38 2021-01-14T23:02:57.0000000 00:00 1213.38 RPM
2021-01-14 20:09:39 1055 1055 90020191 E030214 248 -33.359910 -60.149510 Bs.As. 300196066 35 ... 79.0 C 2021-01-14T23:09:39.0000000 00:00 12.15 2021-01-14T23:09:39.0000000 00:00 15.0 1402.50 2021-01-14T23:09:39.0000000 00:00 1402.50 RPM
2021-01-14 20:15:14 1054 1054 90020192 E030214 248 -33.359943 -60.149450 Bs.As. 300196066 35 ... 85.0 C 2021-01-14T23:15:14.0000000 00:00 12.45 2021-01-14T23:15:14.0000000 00:00 11.0 1238.50 2021-01-14T23:15:14.0000000 00:00 1238.50 RPM
2021-01-14 20:33:05 1053 1053 90020193 E030214 248 -33.360090 -60.149590 Bs.As. 300196066 35 ... 76.0 C 2021-01-14T23:33:05.0000000 00:00 11.95 2021-01-14T23:33:05.0000000 00:00 17.0 1429.75 2021-01-14T23:33:05.0000000 00:00 1429.75 RPM
2021-01-14 20:39:04 1052 1052 90020194 E030214 248 -33.359960 -60.149403 Bs.As. 300196066 35 ... 85.0 C 2021-01-14T23:39:04.0000000 00:00 12.50 2021-01-14T23:39:04.0000000 00:00 17.0 1798.62 2021-01-14T23:39:04.0000000 00:00 1798.62 RPM
2021-01-14 22:51:55 1049 1049 90025252 E030214 248 -33.359820 -60.149357 Bs.As. 300196066 35 ... 46.0 C 2021-01-15T01:51:55.0000000 00:00 11.30 2021-01-15T01:51:55.0000000 00:00 11.0 160.12 2021-01-15T01:51:55.0000000 00:00 160.12 RPM
2021-01-14 22:59:18 1047 1047 90027665 E030214 248 -33.359890 -60.149350 Bs.As. 300196066 35 ... 79.0 C 2021-01-15T01:59:18.0000000 00:00 12.70 2021-01-15T01:59:18.0000000 00:00 16.0 1796.25 2021-01-15T01:59:18.0000000 00:00 1796.25 RPM
2021-01-14 22:59:27 1046 1046 90027666 E030214 248 -33.359890 -60.149350 Bs.As. 300196066 35 ... 74.0 C 2021-01-15T01:59:27.0000000 00:00 13.15 2021-01-15T01:59:27.0000000 00:00 0.0 0.00 2021-01-15T01:59:27.0000000 00:00 0.00 RPM
2021-01-14 23:02:51 1045 1045 90027667 E030214 248 -33.359898 -60.149376 Bs.As. 300196066 35 ... 91.0 C 2021-01-15T02:02:51.0000000 00:00 12.70 2021-01-15T02:02:51.0000000 00:00 21.0 1801.88 2021-01-15T02:02:51.0000000 00:00 1801.88 RPM
2021-01-15 01:30:01 1041 1041 90032261 E030214 248 -33.359966 -60.149550 Bs.As. 300196066 35 ... 44.0 C 2021-01-15T04:30:01.0000000 00:00 12.40 2021-01-15T04:30:01.0000000 00:00 22.0 1396.75 2021-01-15T04:30:01.0000000 00:00 1396.75 RPM
2021-01-15 01:35:54 1040 1040 90032262 E030214 248 -33.359966 -60.149490 Bs.As. 300196066 35 ... 83.0 C 2021-01-15T04:35:54.0000000 00:00 12.95 2021-01-15T04:35:54.0000000 00:00 12.0 1205.50 2021-01-15T04:35:54.0000000 00:00 1205.50 RPM
2021-01-15 01:46:58 1039 1039 90032264 E030214 248 -33.359910 -60.149574 Bs.As. 300196066 35 ... 68.0 C 2021-01-15T04:46:58.0000000 00:00 9.75 2021-01-15T04:46:58.0000000 00:00 4.0 1261.38 2021-01-15T04:46:58.0000000 00:00 1261.38 RPM
2021-01-15 02:11:55 1036 1036 90034688 E030214 248 -33.360000 -60.149150 Bs.As. 300196066 35 ... 84.0 C 2021-01-15T05:11:55.0000000 00:00 12.85 2021-01-15T05:11:55.0000000 00:00 11.0 1207.38 2021-01-15T05:11:55.0000000 00:00 1207.38 RPM

20 rows × 40 columns

In [25]:
bn.tail(10)
Out[25]:
Unnamed: 0 index id_posicion title id_flota lat lng data.flota data.patente data.t_vehiculo ... data.telemetry.EngineCoolantTemp.Temperature data.telemetry.EngineCoolantTemp.UnitOfMeasure data.telemetry.BatteryVoltage.datetime data.telemetry.BatteryVoltage.Voltage data.telemetry.EngineLoad.datetime data.telemetry.EngineLoad.Percent data.telemetry.EngineSpeed.Speed data.telemetry.EngineSpeed.datetime data.telemetry.EngineSpeed.speed data.telemetry.EngineSpeed.UnitOfMeasure
fecha
2021-06-01 12:09:55 192 192 103041784 E030214 248 -33.372044 -60.16205 Bs.As. 300196066 35 ... 85.0 C 2021-06-01T15:09:55.0000000 00:00 12.85 2021-06-01T15:09:55.0000000 00:00 9.0 1199.62 2021-06-01T15:09:55.0000000 00:00 1199.62 RPM
2021-06-01 11:30:09 193 193 103036268 E030214 248 -33.371330 -60.16170 Bs.As. 300196066 35 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2021-06-01 11:30:09 194 194 103036269 E030214 248 -33.371330 -60.16170 Bs.As. 300196066 35 ... 69.0 C 2021-06-01T14:30:09.0000000 00:00 12.40 2021-06-01T14:30:09.0000000 00:00 13.0 1414.00 2021-06-01T14:30:09.0000000 00:00 1414.00 RPM
2021-06-01 11:30:07 195 195 103036267 E030214 248 -33.371330 -60.16170 Bs.As. 300196066 35 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2021-06-01 10:44:22 196 196 103031523 E030214 248 -33.371456 -60.16149 Bs.As. 300196066 35 ... 84.0 C 2021-06-01T13:44:22.0000000 00:00 12.85 2021-06-01T13:44:22.0000000 00:00 8.0 1213.25 2021-06-01T13:44:22.0000000 00:00 1213.25 RPM
2021-06-01 10:03:30 197 197 103031522 E030214 248 -33.366510 -60.15792 Bs.As. 300196066 35 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2021-06-01 10:03:29 198 198 103031521 E030214 248 -33.366510 -60.15792 Bs.As. 300196066 35 ... 14.0 C 2021-06-01T13:03:29.0000000 00:00 11.95 2021-06-01T13:03:29.0000000 00:00 21.0 1563.25 2021-06-01T13:03:29.0000000 00:00 1563.25 RPM
2021-06-01 10:03:24 199 199 103031520 E030214 248 -33.366510 -60.15792 Bs.As. 300196066 35 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2021-06-01 09:00:00 200 200 103025022 E030214 248 -33.366510 -60.15792 Bs.As. 300196066 35 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2021-06-01 03:00:00 201 201 102998843 E030214 248 -33.366330 -60.15790 Bs.As. 300196066 35 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

10 rows × 40 columns

In [4]:
# Esta función lo que hace es recursiva para los json anidados y te saca una 
# Dataframe Plano. 
def flatten_nested_json_df(df):

    df = df.reset_index()

    print(f"original shape: {df.shape}")
    print(f"original columns: {df.columns}")


    # search for columns to explode/flatten
    s = (df.applymap(type) == list).all()
    list_columns = s[s].index.tolist()

    s = (df.applymap(type) == dict).all()
    dict_columns = s[s].index.tolist()

    print(f"lists: {list_columns}, dicts: {dict_columns}")
    while len(list_columns) > 0 or len(dict_columns) > 0:
        new_columns = []

        for col in dict_columns:
            print(f"flattening: {col}")
            # explode dictionaries horizontally, adding new columns
            horiz_exploded = pd.json_normalize(df[col]).add_prefix(f'{col}.')
            horiz_exploded.index = df.index
            df = pd.concat([df, horiz_exploded], axis=1).drop(columns=[col])
            new_columns.extend(horiz_exploded.columns) # inplace

        for col in list_columns:
            print(f"exploding: {col}")
            # explode lists vertically, adding new columns
            df = df.drop(columns=[col]).join(df[col].explode().to_frame())
            new_columns.append(col)

        # check if there are still dict o list fields to flatten
        s = (df[new_columns].applymap(type) == list).all()
        list_columns = s[s].index.tolist()

        s = (df[new_columns].applymap(type) == dict).all()
        dict_columns = s[s].index.tolist()

        print(f"lists: {list_columns}, dicts: {dict_columns}")

    print(f"final shape: {df.shape}")
    print(f"final columns: {df.columns}")
    return df
In [5]:
# Ventana (4)
bn= flatten_nested_json_df(bn)

original shape: (181, 8)
original columns: Index(['index', 'id_posicion', 'fecha', 'title', 'id_flota', 'lat', 'lng',
       'data'],
      dtype='object')
lists: [], dicts: ['data']
flattening: data
lists: [], dicts: []
final shape: (181, 40)
final columns: Index(['index', 'id_posicion', 'fecha', 'title', 'id_flota', 'lat', 'lng',
       'data.flota', 'data.patente', 'data.t_vehiculo', 'data.velocidad',
       'data.rumbo', 'data.altitud', 'data.evento', 'data.color',
       'data.telemetry.EquipmentHeader.UnitInstallDateTime',
       'data.telemetry.EquipmentHeader.OEMName',
       'data.telemetry.EquipmentHeader.Model',
       'data.telemetry.EquipmentHeader.EquipmentID',
       'data.telemetry.EquipmentHeader.SerialNumber',
       'data.telemetry.Location.datetime', 'data.telemetry.Location.Latitude',
       'data.telemetry.Location.Longitude',
       'data.telemetry.CumulativeOperatingHours.datetime',
       'data.telemetry.CumulativeOperatingHours.Hour',
       'data.telemetry.EngineOilPressure.Pressure',
       'data.telemetry.EngineOilPressure.datetime',
       'data.telemetry.EngineOilPressure.pressure',
       'data.telemetry.EngineOilPressure.UnitOfMeasure',
       'data.telemetry.EngineCoolantTemp.datetime',
       'data.telemetry.EngineCoolantTemp.Temperature',
       'data.telemetry.EngineCoolantTemp.UnitOfMeasure',
       'data.telemetry.BatteryVoltage.datetime',
       'data.telemetry.BatteryVoltage.Voltage',
       'data.telemetry.EngineLoad.datetime',
       'data.telemetry.EngineLoad.Percent', 'data.telemetry.EngineSpeed.Speed',
       'data.telemetry.EngineSpeed.datetime',
       'data.telemetry.EngineSpeed.speed',
       'data.telemetry.EngineSpeed.UnitOfMeasure'],
      dtype='object')
In [26]:
bn.describe()
Out[26]:
Unnamed: 0 index id_posicion id_flota lat lng data.patente data.t_vehiculo data.velocidad data.rumbo ... data.telemetry.Location.Latitude data.telemetry.Location.Longitude data.telemetry.CumulativeOperatingHours.Hour data.telemetry.EngineOilPressure.Pressure data.telemetry.EngineOilPressure.pressure data.telemetry.EngineCoolantTemp.Temperature data.telemetry.BatteryVoltage.Voltage data.telemetry.EngineLoad.Percent data.telemetry.EngineSpeed.Speed data.telemetry.EngineSpeed.speed
count 1454.000000 1454.000000 1.454000e+03 1454.000000 1454.000000 1454.000000 1454.0 1454.0 1454.0 1454.0 ... 1454.000000 1454.000000 1032.000000 1032.000000 1032.000000 1032.000000 1032.000000 1032.00000 1032.000000 1032.000000
mean 330.848006 330.848006 9.720750e+07 46291.526823 -34.144168 -59.087865 300196066.0 35.0 0.0 0.0 ... -34.144168 -59.087865 1956.035465 211.573643 211.573643 68.571705 12.231153 15.79845 1313.205678 1313.205678
std 281.145505 281.145505 5.179455e+06 62853.821857 0.531679 0.707370 0.0 0.0 0.0 0.0 ... 0.531679 0.707370 47.371614 102.177440 102.177440 21.799516 0.793298 15.55579 604.180284 604.180284
min 0.000000 0.000000 9.000677e+07 248.000000 -34.877785 -60.162086 300196066.0 35.0 0.0 0.0 ... -34.877785 -60.162086 1861.000000 0.000000 0.000000 6.000000 6.300000 0.00000 0.000000 0.000000
25% 114.000000 114.000000 9.232371e+07 248.000000 -34.655840 -60.149036 300196066.0 35.0 0.0 0.0 ... -34.655840 -60.149036 1901.500000 232.000000 232.000000 57.000000 12.050000 9.00000 1201.120000 1201.120000
50% 246.000000 246.000000 9.945692e+07 248.000000 -34.156696 -58.978565 300196066.0 35.0 0.0 0.0 ... -34.156696 -58.978565 1975.475000 256.000000 256.000000 78.000000 12.400000 13.00000 1407.625000 1407.625000
75% 459.750000 459.750000 1.021664e+08 132034.000000 -33.367172 -58.379830 300196066.0 35.0 0.0 0.0 ... -33.367172 -58.379830 1998.800000 272.000000 272.000000 85.000000 12.750000 19.00000 1799.652500 1799.652500
max 1067.000000 1067.000000 1.062528e+08 132034.000000 -33.359547 -58.378720 300196066.0 35.0 0.0 0.0 ... -33.359547 -58.378720 2012.050000 352.000000 352.000000 93.000000 13.250000 100.00000 2486.500000 2486.500000

8 rows × 22 columns

In [27]:
bn.head(10)
Out[27]:
Unnamed: 0 index id_posicion title id_flota lat lng data.flota data.patente data.t_vehiculo ... data.telemetry.EngineCoolantTemp.Temperature data.telemetry.EngineCoolantTemp.UnitOfMeasure data.telemetry.BatteryVoltage.datetime data.telemetry.BatteryVoltage.Voltage data.telemetry.EngineLoad.datetime data.telemetry.EngineLoad.Percent data.telemetry.EngineSpeed.Speed data.telemetry.EngineSpeed.datetime data.telemetry.EngineSpeed.speed data.telemetry.EngineSpeed.UnitOfMeasure
fecha
2021-01-14 14:37:44 1067 1067 90006767 E030214 248 -33.359980 -60.149075 Bs.As. 300196066 35 ... 85.0 C 2021-01-14T17:37:44.0000000 00:00 12.70 2021-01-14T17:37:44.0000000 00:00 10.0 1199.88 2021-01-14T17:37:44.0000000 00:00 1199.88 RPM
2021-01-14 15:37:44 1066 1066 90006876 E030214 248 -33.359890 -60.149094 Bs.As. 300196066 35 ... 86.0 C 2021-01-14T18:37:44.0000000 00:00 12.70 2021-01-14T18:37:44.0000000 00:00 10.0 1199.25 2021-01-14T18:37:44.0000000 00:00 1199.25 RPM
2021-01-14 16:37:44 1065 1065 90009511 E030214 248 -33.359947 -60.149140 Bs.As. 300196066 35 ... 86.0 C 2021-01-14T19:37:44.0000000 00:00 12.05 2021-01-14T19:37:44.0000000 00:00 37.0 1790.88 2021-01-14T19:37:44.0000000 00:00 1790.88 RPM
2021-01-14 16:49:57 1064 1064 90009512 E030214 248 -33.359890 -60.149185 Bs.As. 300196066 35 ... 87.0 C 2021-01-14T19:49:57.0000000 00:00 12.70 2021-01-14T19:49:57.0000000 00:00 10.0 1204.88 2021-01-14T19:49:57.0000000 00:00 1204.88 RPM
2021-01-14 16:50:48 1063 1063 90009513 E030214 248 -33.359962 -60.148910 Bs.As. 300196066 35 ... 81.0 C 2021-01-14T19:50:48.0000000 00:00 12.10 2021-01-14T19:50:48.0000000 00:00 13.0 1417.62 2021-01-14T19:50:48.0000000 00:00 1417.62 RPM
2021-01-14 16:51:16 1062 1062 90009514 E030214 248 -33.359962 -60.148910 Bs.As. 300196066 35 ... 87.0 C 2021-01-14T19:51:16.0000000 00:00 12.55 2021-01-14T19:51:16.0000000 00:00 10.0 1217.00 2021-01-14T19:51:16.0000000 00:00 1217.00 RPM
2021-01-14 19:46:08 1059 1059 90017717 E030214 248 -33.359730 -60.149017 Bs.As. 300196066 35 ... 47.0 C 2021-01-14T22:46:08.0000000 00:00 12.25 2021-01-14T22:46:08.0000000 00:00 26.0 1799.50 2021-01-14T22:46:08.0000000 00:00 1799.50 RPM
2021-01-14 20:02:57 1058 1058 90020188 E030214 248 -33.359940 -60.149384 Bs.As. 300196066 35 ... 85.0 C 2021-01-14T23:02:57.0000000 00:00 12.65 2021-01-14T23:02:57.0000000 00:00 12.0 1213.38 2021-01-14T23:02:57.0000000 00:00 1213.38 RPM
2021-01-14 20:09:39 1055 1055 90020191 E030214 248 -33.359910 -60.149510 Bs.As. 300196066 35 ... 79.0 C 2021-01-14T23:09:39.0000000 00:00 12.15 2021-01-14T23:09:39.0000000 00:00 15.0 1402.50 2021-01-14T23:09:39.0000000 00:00 1402.50 RPM
2021-01-14 20:15:14 1054 1054 90020192 E030214 248 -33.359943 -60.149450 Bs.As. 300196066 35 ... 85.0 C 2021-01-14T23:15:14.0000000 00:00 12.45 2021-01-14T23:15:14.0000000 00:00 11.0 1238.50 2021-01-14T23:15:14.0000000 00:00 1238.50 RPM

10 rows × 40 columns

In [28]:
bn.tail(10)
Out[28]:
Unnamed: 0 index id_posicion title id_flota lat lng data.flota data.patente data.t_vehiculo ... data.telemetry.EngineCoolantTemp.Temperature data.telemetry.EngineCoolantTemp.UnitOfMeasure data.telemetry.BatteryVoltage.datetime data.telemetry.BatteryVoltage.Voltage data.telemetry.EngineLoad.datetime data.telemetry.EngineLoad.Percent data.telemetry.EngineSpeed.Speed data.telemetry.EngineSpeed.datetime data.telemetry.EngineSpeed.speed data.telemetry.EngineSpeed.UnitOfMeasure
fecha
2021-06-01 12:09:55 192 192 103041784 E030214 248 -33.372044 -60.16205 Bs.As. 300196066 35 ... 85.0 C 2021-06-01T15:09:55.0000000 00:00 12.85 2021-06-01T15:09:55.0000000 00:00 9.0 1199.62 2021-06-01T15:09:55.0000000 00:00 1199.62 RPM
2021-06-01 11:30:09 193 193 103036268 E030214 248 -33.371330 -60.16170 Bs.As. 300196066 35 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2021-06-01 11:30:09 194 194 103036269 E030214 248 -33.371330 -60.16170 Bs.As. 300196066 35 ... 69.0 C 2021-06-01T14:30:09.0000000 00:00 12.40 2021-06-01T14:30:09.0000000 00:00 13.0 1414.00 2021-06-01T14:30:09.0000000 00:00 1414.00 RPM
2021-06-01 11:30:07 195 195 103036267 E030214 248 -33.371330 -60.16170 Bs.As. 300196066 35 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2021-06-01 10:44:22 196 196 103031523 E030214 248 -33.371456 -60.16149 Bs.As. 300196066 35 ... 84.0 C 2021-06-01T13:44:22.0000000 00:00 12.85 2021-06-01T13:44:22.0000000 00:00 8.0 1213.25 2021-06-01T13:44:22.0000000 00:00 1213.25 RPM
2021-06-01 10:03:30 197 197 103031522 E030214 248 -33.366510 -60.15792 Bs.As. 300196066 35 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2021-06-01 10:03:29 198 198 103031521 E030214 248 -33.366510 -60.15792 Bs.As. 300196066 35 ... 14.0 C 2021-06-01T13:03:29.0000000 00:00 11.95 2021-06-01T13:03:29.0000000 00:00 21.0 1563.25 2021-06-01T13:03:29.0000000 00:00 1563.25 RPM
2021-06-01 10:03:24 199 199 103031520 E030214 248 -33.366510 -60.15792 Bs.As. 300196066 35 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2021-06-01 09:00:00 200 200 103025022 E030214 248 -33.366510 -60.15792 Bs.As. 300196066 35 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2021-06-01 03:00:00 201 201 102998843 E030214 248 -33.366330 -60.15790 Bs.As. 300196066 35 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

10 rows × 40 columns

In [29]:
# Ordeno los datos
# Ya vienen ordenado los datos de los .CSV no hace falta ejecutar esta linea , ni otras que ordene.
df = bn
#final_bn = df.sort_values(by=['fecha'], ascending=True)
final_bn = bn
#Puedo querer grabar para auditoria.
#final_bn.to_csv('E030214_07.csv') 
#final_bn
In [30]:
final_bn
Out[30]:
Unnamed: 0 index id_posicion title id_flota lat lng data.flota data.patente data.t_vehiculo ... data.telemetry.EngineCoolantTemp.Temperature data.telemetry.EngineCoolantTemp.UnitOfMeasure data.telemetry.BatteryVoltage.datetime data.telemetry.BatteryVoltage.Voltage data.telemetry.EngineLoad.datetime data.telemetry.EngineLoad.Percent data.telemetry.EngineSpeed.Speed data.telemetry.EngineSpeed.datetime data.telemetry.EngineSpeed.speed data.telemetry.EngineSpeed.UnitOfMeasure
fecha
2021-01-14 14:37:44 1067 1067 90006767 E030214 248 -33.359980 -60.149075 Bs.As. 300196066 35 ... 85.0 C 2021-01-14T17:37:44.0000000 00:00 12.70 2021-01-14T17:37:44.0000000 00:00 10.0 1199.88 2021-01-14T17:37:44.0000000 00:00 1199.88 RPM
2021-01-14 15:37:44 1066 1066 90006876 E030214 248 -33.359890 -60.149094 Bs.As. 300196066 35 ... 86.0 C 2021-01-14T18:37:44.0000000 00:00 12.70 2021-01-14T18:37:44.0000000 00:00 10.0 1199.25 2021-01-14T18:37:44.0000000 00:00 1199.25 RPM
2021-01-14 16:37:44 1065 1065 90009511 E030214 248 -33.359947 -60.149140 Bs.As. 300196066 35 ... 86.0 C 2021-01-14T19:37:44.0000000 00:00 12.05 2021-01-14T19:37:44.0000000 00:00 37.0 1790.88 2021-01-14T19:37:44.0000000 00:00 1790.88 RPM
2021-01-14 16:49:57 1064 1064 90009512 E030214 248 -33.359890 -60.149185 Bs.As. 300196066 35 ... 87.0 C 2021-01-14T19:49:57.0000000 00:00 12.70 2021-01-14T19:49:57.0000000 00:00 10.0 1204.88 2021-01-14T19:49:57.0000000 00:00 1204.88 RPM
2021-01-14 16:50:48 1063 1063 90009513 E030214 248 -33.359962 -60.148910 Bs.As. 300196066 35 ... 81.0 C 2021-01-14T19:50:48.0000000 00:00 12.10 2021-01-14T19:50:48.0000000 00:00 13.0 1417.62 2021-01-14T19:50:48.0000000 00:00 1417.62 RPM
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2021-06-01 10:03:30 197 197 103031522 E030214 248 -33.366510 -60.157920 Bs.As. 300196066 35 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2021-06-01 10:03:29 198 198 103031521 E030214 248 -33.366510 -60.157920 Bs.As. 300196066 35 ... 14.0 C 2021-06-01T13:03:29.0000000 00:00 11.95 2021-06-01T13:03:29.0000000 00:00 21.0 1563.25 2021-06-01T13:03:29.0000000 00:00 1563.25 RPM
2021-06-01 10:03:24 199 199 103031520 E030214 248 -33.366510 -60.157920 Bs.As. 300196066 35 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2021-06-01 09:00:00 200 200 103025022 E030214 248 -33.366510 -60.157920 Bs.As. 300196066 35 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN
2021-06-01 03:00:00 201 201 102998843 E030214 248 -33.366330 -60.157900 Bs.As. 300196066 35 ... NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN

1454 rows × 40 columns

In [31]:
###########################################################
#                 Started  ARIMA MOdel 
#   
#  
##############################################################



#import plotly.express as px
#  Armo el promedio diario de los valores con comando resample ("D")
# Hasta acá el bn tiene valores repetidos por dia y por hora , y tambien contiene valores Nan
# Es decir puedo tener dias sin trabajar , horas sin valor en dias trabajados sin acuse del censor.
#
#  Ojo con el cambio de indices , cuando lees de .csv , el indice ya viene seteado posicion (3)
#  para ejecutar este código de abajo , no ejecutar ninguna ventana hasta la  (4) , hasta esta ventana.
#  si no da error de range (index) , por que se cambia el indice de bn en algún momento de la ejecución posterior
#  de la ventana (4)
#
import seaborn as sns
import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
import pandas as pd
# reindexamos en función de la fecha 
from datetime import datetime


# Lo que hago es resumen por dia valor promedio , resultante puede ser 
# dias que no hay acuse de sensor ó regitros sin valor por que el censor no dio valor.
df = bn.resample('D').mean()

#borro los Nan Promedio
# Aca se tiene registros faltanes por el borrado ó por no operar. 
#  Con la siguiente formula podría rellenarse con valores 0 los dias faltantes , y luego 
#  sobre eso interpolar , para tener toda la secuencia.
#  idex.min , es el valor minimo del actual indice  y lo mismo max. , llena con valores 0
#f = lambda x: x.reindex(pd.date_range(x.index.min(), 
#                                      x.index.max(), name='date'), fill_value=0)
#df = (df.set_index('date')
#        .groupby('product')
#        .apply(f)
#        .drop('product', axis=1)
#        .reset_index())
mydf1 = df.dropna()


%matplotlib inline

#ojo con el eje x , por ej. x='index', cauza prolbemas por la organización de los datos.
# Grafico inicial era 15, 6 , lo baje para poder copiar imagen en la tesis
sns.set(rc={'figure.figsize':(8, 4)})
#mydf1.plot(marker='.', y=['data.telemetry.EngineCoolantTemp.Temperature','data.telemetry.EngineOilPressure.Pressure' ], rot=45)
bn.plot(marker='.', y=['data.telemetry.EngineOilPressure.Pressure' ], rot=45, color = 'black')

mydf1.plot(marker='.', y=['data.telemetry.BatteryVoltage.Voltage' ], rot=45)
mydf1.plot(marker='.', y=['data.telemetry.EngineSpeed.speed' ], rot=45)
mydf1.plot(marker='.', y=['data.telemetry.EngineOilPressure.Pressure' ], rot=45,color = 'black')


#df.plot(marker='.',x='index', y=['data.telemetry.EngineLoad.Percent','data.telemetry.EngineOilPressure.Pressure','data.telemetry.EngineCoolantTemp.Temperature'], figsize=(15,4))

#df.to_csv('revision.csv')
Out[31]:
<matplotlib.axes._subplots.AxesSubplot at 0x242e0954a60>
In [32]:
mydf1.shape
Out[32]:
(68, 22)
In [33]:
# La idea es trabajar con la serie de valores promedios por dia y hacerla stacionaria
myseries = pd.Series(mydf1['data.telemetry.EngineOilPressure.Pressure'])
#define function for ADF test

from statsmodels.tsa.stattools import adfuller
def adf_test(timeseries):
    #Perform Dickey-Fuller test:
    print ('Results of Dickey-Fuller Test:')
    dftest = adfuller(timeseries, autolag='AIC')
    dfoutput = pd.Series(dftest[0:4], index=['Test Statistic','p-value','#Lags Used','Number of Observations Used'])
    for key,value in dftest[4].items():
       dfoutput['Critical Value (%s)'%key] = value
    print (dfoutput)

#apply adf test on the series
adf_test(myseries)
# p-value > 0.05: Fail to reject the null hypothesis (H0), the data has a unit root and is non-stationary.
# p-value <= 0.05: Reject the null hypothesis (H0), the data does not have a unit root and is stationary.
#Test for stationarity: If the test statistic is less than the critical value,
#we can reject the null hypothesis (aka the series is stationary). 
#When the test statistic is greater than the critical value, we fail to reject the null hypothesis
#(which means the series is not stationary)

Results of Dickey-Fuller Test:
Test Statistic                 -4.583291
p-value                         0.000139
#Lags Used                      0.000000
Number of Observations Used    67.000000
Critical Value (1%)            -3.531955
Critical Value (5%)            -2.905755
Critical Value (10%)           -2.590357
dtype: float64
In [34]:
# Es el promedio por dia de valores Kpa
mydf1.describe()
Out[34]:
Unnamed: 0 index id_posicion id_flota lat lng data.patente data.t_vehiculo data.velocidad data.rumbo ... data.telemetry.Location.Latitude data.telemetry.Location.Longitude data.telemetry.CumulativeOperatingHours.Hour data.telemetry.EngineOilPressure.Pressure data.telemetry.EngineOilPressure.pressure data.telemetry.EngineCoolantTemp.Temperature data.telemetry.BatteryVoltage.Voltage data.telemetry.EngineLoad.Percent data.telemetry.EngineSpeed.Speed data.telemetry.EngineSpeed.speed
count 68.000000 68.000000 6.800000e+01 68.000000 68.000000 68.000000 68.0 68.0 68.0 68.0 ... 68.000000 68.000000 68.000000 68.000000 68.000000 68.000000 68.000000 68.000000 68.000000 68.000000
mean 369.782514 369.782514 9.553391e+07 23504.352941 -34.260662 -58.924162 300196066.0 35.0 0.0 0.0 ... -34.260662 -58.924162 1955.862165 199.789465 199.789465 60.890916 12.155963 15.519005 1240.461834 1240.461834
std 298.916472 298.916472 4.803366e+06 50612.997093 0.475266 0.620418 0.0 0.0 0.0 0.0 ... 0.475266 0.620418 48.776462 49.675719 49.675719 17.867302 0.437400 6.259237 321.514673 321.514673
min 15.000000 15.000000 9.001741e+07 248.000000 -34.877522 -60.160883 300196066.0 35.0 0.0 0.0 ... -34.877522 -60.160883 1863.234375 0.000000 0.000000 11.000000 9.575000 0.000000 0.000000 0.000000
25% 109.732143 109.732143 9.166670e+07 248.000000 -34.656055 -58.979930 300196066.0 35.0 0.0 0.0 ... -34.656055 -58.979930 1895.121875 164.266667 164.266667 48.052885 11.993750 12.125000 1080.628875 1080.628875
50% 298.802632 298.802632 9.323284e+07 248.000000 -34.156780 -58.972374 300196066.0 35.0 0.0 0.0 ... -34.156780 -58.972374 1980.820089 208.562500 208.562500 64.952381 12.283333 14.445652 1299.975615 1299.975615
75% 594.750000 594.750000 9.981429e+07 248.000000 -34.155173 -58.379640 300196066.0 35.0 0.0 0.0 ... -34.155173 -58.379640 1999.328810 237.478261 237.478261 75.890625 12.420729 18.764170 1431.646250 1431.646250
max 1056.562500 1056.562500 1.049034e+08 132034.000000 -33.359919 -58.378912 300196066.0 35.0 0.0 0.0 ... -33.359919 -58.378912 2011.595652 273.473684 273.473684 82.287879 12.650000 39.500000 1795.560000 1795.560000

8 rows × 22 columns

In [35]:
#define function for kpss test
from statsmodels.tsa.stattools import kpss
#define KPSS
def kpss_test(timeseries):
    print ('Results of KPSS Test:')
    kpsstest = kpss(timeseries, regression='c')
    kpss_output = pd.Series(kpsstest[0:3], index=['Test Statistic','p-value','Lags Used'])
    for key,value in kpsstest[3].items():
        kpss_output['Critical Value (%s)'%key] = value
    print (kpss_output)

kpss_test(myseries)

Results of KPSS Test:
Test Statistic            0.415660
p-value                   0.070405
Lags Used                11.000000
Critical Value (10%)      0.347000
Critical Value (5%)       0.463000
Critical Value (2.5%)     0.574000
Critical Value (1%)       0.739000
dtype: float64

C:\Users\minda\Anaconda3\lib\site-packages\statsmodels\tsa\stattools.py:1661: FutureWarning: The behavior of using lags=None will change in the next release. Currently lags=None is the same as lags='legacy', and so a sample-size lag length is used. After the next release, the default will change to be the same as lags='auto' which uses an automatic lag length selection method. To silence this warning, either use 'auto' or 'legacy'
  warn(msg, FutureWarning)
In [36]:
from pandas.plotting import autocorrelation_plot
autocorrelation_plot(mydf1['data.telemetry.EngineOilPressure.Pressure'], color = 'black')
plt.show()
In [37]:
#
#  Para graficar para la tesis , hacerlo por separado comentando ambas lineas ax1 y fig , ax2 , fig
#
from statsmodels.graphics.tsaplots import plot_acf,plot_pacf
import statsmodels.api as sm
fig = plt.figure(figsize=(8,8))
ax1 = fig.add_subplot(211)
fig = sm.graphics.tsa.plot_acf(mydf1['data.telemetry.EngineOilPressure.Pressure'].dropna(),lags=10,ax=ax1)

ax2 = fig.add_subplot(212)
fig = sm.graphics.tsa.plot_pacf(mydf1['data.telemetry.EngineOilPressure.Pressure'].dropna(),lags=10,ax=ax2)
In [43]:
# Con esta función se encuentra la mejor combinación de p, d , q 
# para modelo ARIMA , el punto es que requiere una verión más 
# baja de python 3.7 , y estamos en la 3.8.
import pmdarima as pmd

def arimamodel(timeseriesarray):
    autoarima_model = pmd.auto_arima(timeseriesarray, 
                              start_p=1, 
                              start_q=1,
                              test="adf",
                              trace=True)
    return autoarima_mode
arima_model = arimamodel(myseries)
arima_model.summary()

Performing stepwise search to minimize aic
 ARIMA(1,1,1)(0,0,0)[0] intercept   : AIC=697.713, Time=0.15 sec
 ARIMA(0,1,0)(0,0,0)[0] intercept   : AIC=715.760, Time=0.00 sec
 ARIMA(1,1,0)(0,0,0)[0] intercept   : AIC=705.953, Time=0.02 sec
 ARIMA(0,1,1)(0,0,0)[0] intercept   : AIC=697.360, Time=0.02 sec
 ARIMA(0,1,0)(0,0,0)[0]             : AIC=713.760, Time=0.00 sec
 ARIMA(0,1,2)(0,0,0)[0] intercept   : AIC=698.136, Time=0.04 sec
 ARIMA(1,1,2)(0,0,0)[0] intercept   : AIC=inf, Time=0.13 sec
 ARIMA(0,1,1)(0,0,0)[0]             : AIC=695.686, Time=0.02 sec
 ARIMA(1,1,1)(0,0,0)[0]             : AIC=696.470, Time=0.03 sec
 ARIMA(0,1,2)(0,0,0)[0]             : AIC=696.731, Time=0.03 sec
 ARIMA(1,1,0)(0,0,0)[0]             : AIC=703.959, Time=0.01 sec
 ARIMA(1,1,2)(0,0,0)[0]             : AIC=698.360, Time=0.06 sec

Best model:  ARIMA(0,1,1)(0,0,0)[0]          
Total fit time: 0.528 seconds

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-43-4b74d85f4ff1> in <module>
     11                               trace=True)
     12     return autoarima_mode
---> 13 arima_model = arimamodel(myseries)
     14 arima_model.summary()

<ipython-input-43-4b74d85f4ff1> in arimamodel(timeseriesarray)
     10                               test="adf",
     11                               trace=True)
---> 12     return autoarima_mode
     13 arima_model = arimamodel(myseries)
     14 arima_model.summary()

NameError: name 'autoarima_mode' is not defined
In [51]:
# For non-seasonal data

from statsmodels.tsa.arima_model import ARIMA
model=ARIMA(mydf1['data.telemetry.EngineOilPressure.Pressure'],order=(0,1,1))
model_fit=model.fit(disp=0)
model_fit.summary()

C:\Users\minda\Anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:216: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  warnings.warn('A date index has been provided, but it has no'
C:\Users\minda\Anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:216: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  warnings.warn('A date index has been provided, but it has no'
Out[51]:
ARIMA Model Results
Dep. Variable: D.data.telemetry.EngineOilPressure.Pressure No. Observations: 67
Model: ARIMA(0, 1, 1) Log Likelihood -345.682
Method: css-mle S.D. of innovations 41.875
Date: Sun, 01 Aug 2021 AIC 697.364
Time: 07:18:46 BIC 703.979
Sample: 1 HQIC 699.982
coef std err z P>|z| [0.025 0.975]
const -0.8618 1.426 -0.605 0.545 -3.656 1.932
ma.L1.D.data.telemetry.EngineOilPressure.Pressure -0.7354 0.121 -6.098 0.000 -0.972 -0.499
Roots
Real Imaginary Modulus Frequency
MA.1 1.3599 +0.0000j 1.3599 0.0000
In [60]:
model_fit.plot_predict(dynamic=False)
plt.title('Prediction Vs Testing ')

plt.xlabel('time (days)')
plt.ylabel('Pressure [Kpa]')
plt.show()
In [65]:
# line plot of residuals
from pandas import DataFrame
fig = plt.figure(figsize=(6,4))


residuals = DataFrame(model_fit.resid)
residuals.columns =['Errors Values']
#
residuals.plot( color ='black')
#--
plt.title('Residual')

#--
plt.show()
# density plot of residuals

residuals.plot(kind='kde', title= 'Density', color = 'black')
plt.show()
# summary stats of residuals
#print(residuals.describe())

<Figure size 432x288 with 0 Axes>
In [1]:
# evaluate an ARIMA model using a walk-forward validation
# Este procedimiento , evalua los valores pronosticados , con referencia a los valores 
# pasados y lo gráfica. ( manera burda de hacerlo segun el autor.)
from pandas import read_csv
from pandas import datetime
from matplotlib import pyplot
from statsmodels.tsa.arima.model import ARIMA
from sklearn.metrics import mean_squared_error
from math import sqrt
# load dataset
# split into train and test sets

X = mydf1['data.telemetry.EngineOilPressure.Pressure']
size = int(len(X) * 0.66)
train, test = X[0:size], X[size:len(X)]
history = [x for x in train]
predictions = list()
# walk-forward validation
for t in range(len(test)):
    model = ARIMA(history, order=(0,1,1))
    model_fit = model.fit()
    output = model_fit.forecast()
    yhat = output[0]
    predictions.append(yhat)
    obs = test[t]
    history.append(obs)
    print('predicted=%f, expected=%f' % (yhat, obs))
# evaluate forecasts
rmse = sqrt(mean_squared_error(test, predictions))
print('Test RMSE: %.3f' % rmse)
# plot forecasts against actual outcomes
plt.xticks(rotation=45)
plt.plot(test.index,test ,title='Test data', color = 'gray'  )
plt.show()

plt.plot(test.index,predictions, title='Prediction data ', color='black' )
plt.xticks(rotation=45)
plt.show()

<ipython-input-1-74cf34a58c1d>:5: FutureWarning: The pandas.datetime class is deprecated and will be removed from pandas in a future version. Import from datetime module instead.
  from pandas import datetime

---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-74cf34a58c1d> in <module>
     11 # split into train and test sets
     12 
---> 13 X = mydf1['data.telemetry.EngineOilPressure.Pressure']
     14 size = int(len(X) * 0.66)
     15 train, test = X[0:size], X[size:len(X)]

NameError: name 'mydf1' is not defined
In [70]:
mydf1.shape
Out[70]:
(68, 24)
In [73]:
# For non-seasonal data
#p=1, d=1, q=0 or 1

from statsmodels.tsa.arima_model import ARIMA
model=ARIMA(mydf1['data.telemetry.EngineOilPressure.Pressure'],order=(0,1,1))
model_fit=model.fit()
model_fit.summary()

C:\Users\minda\Anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:216: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  warnings.warn('A date index has been provided, but it has no'
C:\Users\minda\Anaconda3\lib\site-packages\statsmodels\tsa\base\tsa_model.py:216: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.
  warnings.warn('A date index has been provided, but it has no'
Out[73]:
ARIMA Model Results
Dep. Variable: D.data.telemetry.EngineOilPressure.Pressure No. Observations: 67
Model: ARIMA(0, 1, 1) Log Likelihood -345.682
Method: css-mle S.D. of innovations 41.875
Date: Sun, 01 Aug 2021 AIC 697.364
Time: 08:11:33 BIC 703.979
Sample: 1 HQIC 699.982
coef std err z P>|z| [0.025 0.975]
const -0.8618 1.426 -0.605 0.545 -3.656 1.932
ma.L1.D.data.telemetry.EngineOilPressure.Pressure -0.7354 0.121 -6.098 0.000 -0.972 -0.499
Roots
Real Imaginary Modulus Frequency
MA.1 1.3599 +0.0000j 1.3599 0.0000
In [74]:
from statsmodels.tsa.stattools import acf
# 75 y 25 models train and test, por que no se tienen muchas muestras.
# Create Training and Test
train = mydf1['data.telemetry.EngineOilPressure.Pressure'][:50]
test =  mydf1['data.telemetry.EngineOilPressure.Pressure'][50:]
In [75]:
# Forecast
fc, se, conf = model_fit.forecast(18, alpha=0.05)  # 95% conf
In [76]:
# Make as pandas series
fc_series = pd.Series(fc, index=test.index)
lower_series = pd.Series(conf[:, 0], index=test.index)
upper_series = pd.Series(conf[:, 1], index=test.index)
In [83]:
# Plot
plt.figure(figsize=(6,4), dpi=90)
plt.plot(train, label='training')
plt.plot(test, label='actual')
plt.plot(fc_series, label='forecast')
plt.fill_between(lower_series.index, lower_series, upper_series, 
                 color='k', alpha=.15)
plt.xticks(rotation=45)
plt.title('Forecast ')
plt.legend(loc='upper left', fontsize=8)
plt.show()
In [84]:
def forecast_accuracy(forecast, actual):
    mape = np.mean(np.abs(forecast - actual)/np.abs(actual))  # MAPE
    me = np.mean(forecast - actual)             # ME
    mae = np.mean(np.abs(forecast - actual))    # MAE
    mpe = np.mean((forecast - actual)/actual)   # MPE
    rmse = np.mean((forecast - actual)**2)**.5  # RMSE
    corr = np.corrcoef(forecast, actual)[0,1]   # corr
    mins = np.amin(np.hstack([forecast[:,None], 
                              actual[:,None]]), axis=1)
    maxs = np.amax(np.hstack([forecast[:,None], 
                              actual[:,None]]), axis=1)
    minmax = 1 - np.mean(mins/maxs)             # minmax
    acf1 = acf(fc-test)[1]                      # ACF1
    return({'mape':mape, 'me':me, 'mae': mae, 
            'mpe': mpe, 'rmse':rmse, 'acf1':acf1, 
            'corr':corr, 'minmax':minmax})

forecast_accuracy(fc_series, test.values)

C:\Users\minda\Anaconda3\lib\site-packages\statsmodels\tsa\stattools.py:568: FutureWarning: fft=True will become the default in a future version of statsmodels. To suppress this warning, explicitly set fft=False.
  warnings.warn(
Out[84]:
{'mape': inf,
 'me': -7.534991435699841,
 'mae': 35.70644180759616,
 'mpe': inf,
 'rmse': 51.15166715670136,
 'acf1': 0.1975574918863588,
 'corr': 0.28410858178301474,
 'minmax': 0.19955761345213086}
In [ ]:
###############################################################
#                          Finished ARIMA MOdel 
#  
###############################################################
In [59]:
###############################################################
#                          Start PCA - Analysis 
#  
###############################################################
Out[59]:
(1454, 40)
In [85]:
from datetime import date,datetime,timedelta

import matplotlib.pyplot as plt
import matplotlib.ticker as mtick
import numpy as np
import pandas as pd

#import plotly.express as px
import seaborn as sns
# Use seaborn style defaults and set the default figure size

import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt

sns.set(rc={'figure.figsize':(15, 6)})
#final_bn.plot(subplots=True, figsize=(15,4))
#final_bn.plot(marker='.', x='fecha', y=['data.telemetry.EngineCoolantTemp.Temperature','data.telemetry.EngineOilPressure.Pressure', 'data.telemetry.BatteryVoltage.Voltage'], rot=45)
#final_bn.sort_index(ascending=True)
#Ojo , por que con el E040128 no tiene voltage y carga , y con el E030426 no tiene carga

final_bn.plot( y=['data.telemetry.BatteryVoltage.Voltage','data.telemetry.EngineOilPressure.Pressure','data.telemetry.EngineCoolantTemp.Temperature'], figsize=(15,4))

mydf1.plot( y=['data.telemetry.EngineOilPressure.Pressure'],figsize=(15,6),linestyle='-', marker='s', markerfacecolor='b',color='y',markersize=11)
plt.xlabel('Values Date')
plt.ylabel('Pressure')


#plot(SMA10)
#final_bn.plot.line(x='fecha', y='data.telemetry.EngineOilPressure.Pressure', rot=45)
#fig =final_bn.plot.line(x='fecha', y='data.telemetry.EngineCoolantTemp.Temperature', rot=45)
#fig =final_bn.plot.line(x='fecha', y='data.telemetry.BatteryVoltage.Voltage', rot=45)
#fig =final_bn.plot.line(x='fecha', y='data.telemetry.EngineSpeed.speed', rot=45)


#Mydf = bn
Out[85]:
Text(0, 0.5, 'Pressure')
In [62]:
mydf1.describe()
mydf1.head(4)
type(mydf1)
#print(mydf1.fecha)
#print (mydf1.data.telemetry.EngineCoolantTemp.Temperature)
Out[62]:
pandas.core.frame.DataFrame
In [63]:
from statsmodels.tsa.seasonal import seasonal_decompose

#mydf1 = mydf1.set_index(['fecha'])
decomp = seasonal_decompose(x=mydf1[["data.telemetry.EngineOilPressure.pressure"]], model='additive',period = 1)
est_trend = decomp.trend
est_seasonal = decomp.seasonal

fig, axes = plt.subplots(3, 1)
fig.set_figheight(10)
fig.set_figwidth(15)
axes[0].plot(mydf1["data.telemetry.EngineOilPressure.pressure"], label='Original')
axes[0].legend()
axes[1].plot(est_trend, label='Trend',color="b")
axes[1].legend()
axes[2].plot(est_seasonal, label='Seasonality',color='r')
axes[2].legend()
Out[63]:
<matplotlib.legend.Legend at 0x155ce5167c0>
In [94]:
final_bn.shape
# Esta parte es importante Correr para la segunda parte de 
# Clustering , elimina los Nan para la correlación necesaria para PCA
final_bn.dropna(subset = ["data.telemetry.EngineOilPressure.pressure"], inplace=True)
final_bn.shape
Out[94]:
(1032, 40)
In [95]:
final_bn.shape
#final_bn.set_index(['fecha'])
Out[95]:
(1032, 40)
In [96]:
final_bn
Out[96]:
Unnamed: 0 index id_posicion title id_flota lat lng data.flota data.patente data.t_vehiculo ... data.telemetry.EngineCoolantTemp.Temperature data.telemetry.EngineCoolantTemp.UnitOfMeasure data.telemetry.BatteryVoltage.datetime data.telemetry.BatteryVoltage.Voltage data.telemetry.EngineLoad.datetime data.telemetry.EngineLoad.Percent data.telemetry.EngineSpeed.Speed data.telemetry.EngineSpeed.datetime data.telemetry.EngineSpeed.speed data.telemetry.EngineSpeed.UnitOfMeasure
fecha
2021-01-14 14:37:44 1067 1067 90006767 E030214 248 -33.359980 -60.149075 Bs.As. 300196066 35 ... 85.0 C 2021-01-14T17:37:44.0000000 00:00 12.70 2021-01-14T17:37:44.0000000 00:00 10.0 1199.88 2021-01-14T17:37:44.0000000 00:00 1199.88 RPM
2021-01-14 15:37:44 1066 1066 90006876 E030214 248 -33.359890 -60.149094 Bs.As. 300196066 35 ... 86.0 C 2021-01-14T18:37:44.0000000 00:00 12.70 2021-01-14T18:37:44.0000000 00:00 10.0 1199.25 2021-01-14T18:37:44.0000000 00:00 1199.25 RPM
2021-01-14 16:37:44 1065 1065 90009511 E030214 248 -33.359947 -60.149140 Bs.As. 300196066 35 ... 86.0 C 2021-01-14T19:37:44.0000000 00:00 12.05 2021-01-14T19:37:44.0000000 00:00 37.0 1790.88 2021-01-14T19:37:44.0000000 00:00 1790.88 RPM
2021-01-14 16:49:57 1064 1064 90009512 E030214 248 -33.359890 -60.149185 Bs.As. 300196066 35 ... 87.0 C 2021-01-14T19:49:57.0000000 00:00 12.70 2021-01-14T19:49:57.0000000 00:00 10.0 1204.88 2021-01-14T19:49:57.0000000 00:00 1204.88 RPM
2021-01-14 16:50:48 1063 1063 90009513 E030214 248 -33.359962 -60.148910 Bs.As. 300196066 35 ... 81.0 C 2021-01-14T19:50:48.0000000 00:00 12.10 2021-01-14T19:50:48.0000000 00:00 13.0 1417.62 2021-01-14T19:50:48.0000000 00:00 1417.62 RPM
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
2021-06-01 12:11:57 191 191 103041785 E030214 248 -33.372242 -60.162020 Bs.As. 300196066 35 ... 77.0 C 2021-06-01T15:11:57.0000000 00:00 12.80 2021-06-01T15:11:57.0000000 00:00 0.0 0.00 2021-06-01T15:11:57.0000000 00:00 0.00 RPM
2021-06-01 12:09:55 192 192 103041784 E030214 248 -33.372044 -60.162050 Bs.As. 300196066 35 ... 85.0 C 2021-06-01T15:09:55.0000000 00:00 12.85 2021-06-01T15:09:55.0000000 00:00 9.0 1199.62 2021-06-01T15:09:55.0000000 00:00 1199.62 RPM
2021-06-01 11:30:09 194 194 103036269 E030214 248 -33.371330 -60.161700 Bs.As. 300196066 35 ... 69.0 C 2021-06-01T14:30:09.0000000 00:00 12.40 2021-06-01T14:30:09.0000000 00:00 13.0 1414.00 2021-06-01T14:30:09.0000000 00:00 1414.00 RPM
2021-06-01 10:44:22 196 196 103031523 E030214 248 -33.371456 -60.161490 Bs.As. 300196066 35 ... 84.0 C 2021-06-01T13:44:22.0000000 00:00 12.85 2021-06-01T13:44:22.0000000 00:00 8.0 1213.25 2021-06-01T13:44:22.0000000 00:00 1213.25 RPM
2021-06-01 10:03:29 198 198 103031521 E030214 248 -33.366510 -60.157920 Bs.As. 300196066 35 ... 14.0 C 2021-06-01T13:03:29.0000000 00:00 11.95 2021-06-01T13:03:29.0000000 00:00 21.0 1563.25 2021-06-01T13:03:29.0000000 00:00 1563.25 RPM

1032 rows × 40 columns

In [97]:
final_bn.shape
Out[97]:
(1032, 40)
In [98]:
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
#Antes de gráficar , eliminamos los valores Nan

plt.style.use('ggplot')
final_bn.hist(column = ['data.telemetry.BatteryVoltage.Voltage','data.telemetry.EngineOilPressure.pressure', \
                   'data.telemetry.EngineCoolantTemp.Temperature', 'data.telemetry.EngineLoad.Percent', \
                   'data.telemetry.EngineSpeed.Speed','data.telemetry.CumulativeOperatingHours.Hour'],color='lightblue', figsize=[10,10])
# Ojo que el código anterio va bien por E030214  , pero no para el E040128 modelos diferetnes , no tiene voltage y carga , pero 
## por ej . tiene unos error severtity.
#final_bn.hist(column = ['data.telemetry.EngineOilPressure.pressure', \
##                   'data.telemetry.EngineCoolantTemp.Temperature', \
#                   'data.telemetry.EngineSpeed.Speed','data.telemetry.CumulativeOperatingHours.Hour'], figsize=[10,10])

plt.show()
In [99]:
import pandas as pd
%matplotlib inline
import matplotlib.pyplot as plt
# NOrmalización de Variables.
#Antes de gráficar , vamos a normalizar la variable voltage

final_bn["data.telemetry.BatteryVoltage.Voltage"]=((final_bn["data.telemetry.BatteryVoltage.Voltage"]  \
            -final_bn["data.telemetry.BatteryVoltage.Voltage"].min())/(final_bn["data.telemetry.BatteryVoltage.Voltage"].max()-final_bn["data.telemetry.BatteryVoltage.Voltage"].min()))
final_bn["data.telemetry.EngineOilPressure.pressure"]=((final_bn["data.telemetry.EngineOilPressure.pressure"]  \
            -final_bn["data.telemetry.EngineOilPressure.pressure"].min())/(final_bn["data.telemetry.EngineOilPressure.pressure"].max()-final_bn["data.telemetry.EngineOilPressure.pressure"].min()))
final_bn["data.telemetry.EngineCoolantTemp.Temperature"]=((final_bn["data.telemetry.EngineCoolantTemp.Temperature"]  \
            -final_bn["data.telemetry.EngineCoolantTemp.Temperature"].min())/(final_bn["data.telemetry.EngineCoolantTemp.Temperature"].max()-final_bn["data.telemetry.EngineCoolantTemp.Temperature"].min()))


final_bn["data.telemetry.EngineLoad.Percent"]=((final_bn["data.telemetry.EngineLoad.Percent"]  \
            -final_bn["data.telemetry.EngineLoad.Percent"].min())/(final_bn["data.telemetry.EngineLoad.Percent"].max()-final_bn["data.telemetry.EngineLoad.Percent"].min()))
final_bn["data.telemetry.EngineSpeed.Speed"]=((final_bn["data.telemetry.EngineSpeed.Speed"]  \
            -final_bn["data.telemetry.EngineSpeed.Speed"].min())/(final_bn["data.telemetry.EngineSpeed.Speed"].max()-final_bn["data.telemetry.EngineSpeed.Speed"].min()))

final_bn["data.telemetry.CumulativeOperatingHours.Hour"]=((final_bn["data.telemetry.CumulativeOperatingHours.Hour"]  \
            -final_bn["data.telemetry.CumulativeOperatingHours.Hour"].min())/(final_bn["data.telemetry.CumulativeOperatingHours.Hour"].max()-final_bn["data.telemetry.CumulativeOperatingHours.Hour"].min()))

#final_bn["data.telemetry.FuelUsed.FuelConsumed"]=((final_bn["data.telemetry.FuelUsed.FuelConsumed"]  \
#            -final_bn["data.telemetry.FuelUsed.FuelConsumed"].min())/(final_bn["data.telemetry.FuelUsed.FuelConsumed"].max()-final_bn["data.telemetry.FuelUsed.FuelConsumed"].min()))


plt.style.use('ggplot')
#final_bn.hist(column = ['data.telemetry.BatteryVoltage.Voltage','data.telemetry.EngineOilPressure.pressure', \
#                   'data.telemetry.EngineCoolantTemp.Temperature','data.telemetry.EngineLoad.Percent',  \
#                   'data.telemetry.EngineSpeed.Speed','data.telemetry.CumulativeOperatingHours.Hour'], figsize=[10,10])

final_bn.hist(column = ['data.telemetry.EngineOilPressure.pressure', \
                   'data.telemetry.EngineCoolantTemp.Temperature', \
                   'data.telemetry.EngineSpeed.Speed','data.telemetry.CumulativeOperatingHours.Hour'],color='lightblue', figsize=[10,10])

plt.show()
In [105]:
# Elijo las variable que quiero plotear/seguir/medir
from pandas.plotting import scatter_matrix 
Clus_01 = final_bn[['data.telemetry.BatteryVoltage.Voltage','data.telemetry.EngineOilPressure.pressure', \
                    'data.telemetry.EngineCoolantTemp.Temperature','data.telemetry.EngineLoad.Percent',  \
                    'data.telemetry.EngineSpeed.Speed','data.telemetry.CumulativeOperatingHours.Hour']]
Clus_01.columns= ['Voltage','Pressure','Temp.','Percent','Speed','Hour']
#Clus_01 = final_bn[['data.telemetry.EngineOilPressure.pressure', \
#                   'data.telemetry.EngineCoolantTemp.Temperature', \
#                   'data.telemetry.EngineSpeed.Speed','data.telemetry.CumulativeOperatingHours.Hour']]

# esta parte de código me elimina los Nan values
# esto es una posibilidad , la otra es la interpolate abajo.. investigar
#Clus_01.interpolate()
# Podriamos Aplicar metodos de interpolación antes de borrar los registros con valor Nan
Clus_01.dropna(inplace = False , axis = 1 )

#Clus_01.interpolate()
#Clus_01['data.telemetry.EngineOilPressure.pressure'].interpolate()

fig, ax = plt.subplots(figsize=(12,12))
scatter_matrix(Clus_01, alpha=1, ax=ax)
#** habria que corregir los letreros en 45 grados.
#scatter_matrix(Clus_01, figsize=(10,10))
plt.xticks(rotation=180)
plt.yticks(rotation=180)
plt.show()
# Punto de Control que esta grabando.

<ipython-input-105-abb0feef4577>:21: UserWarning: To output multiple subplots, the figure containing the passed axes is being cleared
  scatter_matrix(Clus_01, alpha=1, ax=ax)
In [106]:
# **La escala en la libreria seaborn , no me funciona ó habría que ver como setear. 
#sns.pairplot(Clus_02)
import numpy as np
from sklearn.decomposition import PCA
from sklearn import datasets
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import MinMaxScaler

import matplotlib.pyplot as plt
import seaborn as sns

X_scaled = StandardScaler().fit_transform(Clus_01)

cov = np.cov(X_scaled.T)
autovalores, autovectores = np.linalg.eig(cov)
print(autovalores)
print (autovectores)
# Se podria decir que un PC1 es explicado  por la conjunción de Presion , Velocidad y Voltage en ese orden 
# y que el PC2 es explicado por Percen + temper+Voltage , y que el PC3 es explicado por Hours Cumulated
#hay que ordenra los autovalores de mayor a menor.
#Clus_02.hist( figsize=[10,10])
#plt.show()

[2.54273188 1.34560389 0.09649086 0.6051624  0.43889351 0.97693705]
[[-0.35595271 -0.47559152  0.13730512 -0.76047654  0.22041451 -0.03674976]
 [-0.58827959  0.02128561 -0.71075676  0.02160653 -0.36849502  0.10974265]
 [-0.30747924 -0.56583388  0.0212553   0.62880516  0.41940505 -0.11636244]
 [-0.31614826  0.62338434 -0.08059304 -0.0537833   0.70700017  0.04695881]
 [-0.56312892  0.203618    0.68450604  0.14981628 -0.35264868  0.16146521]
 [ 0.12496961 -0.15206357 -0.02183098  0.02179654  0.12457103  0.9720029 ]]
In [107]:
# get correlation matrix
import numpy as np
corr = Clus_01.corr()
fig, ax = plt.subplots()
# create heatmap
im = ax.imshow(corr.values)
ax.set_xticks(np.arange(len(corr.columns)))
ax.set_yticks(np.arange(len(corr.columns)))
ax.set_xticklabels(corr.columns)
ax.set_yticklabels(corr.columns)
# Rotate the tick labels and set their alignment.
plt.setp(ax.get_xticklabels(), rotation=45, ha="right",
         rotation_mode="anchor")
# Loop over data dimensions and create text annotations.
for i in range(len(corr.columns)):
    for j in range(len(corr.columns)):
        text = ax.text(j, i, np.around(corr.iloc[i, j], decimals=2),
ha="center", va="center", color="white")
In [108]:
Clus_01.shape
Out[108]:
(1032, 6)
In [109]:
import seaborn as sns
sns.heatmap(corr.corr(), center = 0 , annot=True, cmap="Blues")
Out[109]:
<matplotlib.axes._subplots.AxesSubplot at 0x242e61edac0>
In [110]:
# vamos aplicar PCA ( Principal Component Analisis )
# reduce of dimensionality.
from sklearn.decomposition import PCA
#defino cuantos compontentes quiero.
pca = PCA(n_components=6)
principal_compon = pca.fit_transform(Clus_01)

# como elegir cuantos componentes , explican el 95 de la variaciones
plt.plot(np.cumsum(pca.explained_variance_ratio_),color = 'black')
plt.xlabel('number of components')
plt.ylabel('cumulative explained variance')

print (pca.explained_variance_ratio_)
# LOs autovalores abajo.
print('Explained Variance = ', pca.explained_variance_)
print('Principal Components = ', pca.components_)

[0.45686891 0.27926623 0.16893824 0.04882108 0.02927285 0.01683269]
Explained Variance =  [0.15609774 0.09541649 0.05772088 0.01668063 0.01000161 0.0057512 ]
Principal Components =  [[-0.13183227 -0.69903321 -0.31506378 -0.17450217 -0.55759262  0.23096991]
 [-0.03527707 -0.16880888 -0.08112396  0.02207176 -0.14327536 -0.97090834]
 [-0.13576549  0.13170711 -0.87983883  0.34437561  0.26632767  0.02407527]
 [-0.33242141 -0.37245065  0.34215053  0.78453985  0.12631736  0.04744138]
 [-0.74944672 -0.12852475  0.04695207 -0.46895907  0.44579782 -0.03079294]
 [ 0.53923013 -0.55701999 -0.02767446 -0.12264534  0.61882182 -0.01453937]]
In [111]:
#  Como regresamos a las variables originales de un PCA
#  Ojo que el regreso es a valores standarizados a normales
#  si queremos los datos originales , no se tendria que aplicar la función :X_scaled = StandardScaler().fit_transform(Clus_01)
#  4 ventantas anterior.
import numpy as np
import sklearn.datasets, sklearn.decomposition

#X = sklearn.datasets.load_iris().data
#  Clus01 viene con el set de datos.
mu = np.mean(Clus_01, axis=0)

#pca = sklearn.decomposition.PCA()
#pca.fit(X)

nComp = 6
#Xhat = np.dot(pca.fit_transform(Clus_01)[:,:nComp], pca.components_[:nComp,:])
Xhat = np.dot(principal_compon[:,:nComp], pca.components_[:nComp,:])

Xhat += mu
# Reconstruyo los valores de cada fila del sample.
print(Xhat[0,])
print(Xhat[1,])

[9.20863309e-01 6.59090909e-01 9.08045977e-01 1.00000000e-01
 4.82557812e-01 5.55111512e-16]
[0.92086331 0.65909091 0.91954023 0.1        0.48230444 0.00662032]
In [112]:
#print ( principalDf)

pca = PCA(n_components=2)
principal_compon = pca.fit_transform(Clus_01)
principalDf = pd.DataFrame(data = principal_compon, columns = ['principal component 1', 'principal component 2'])

# como elegir cuantos componentes , explican el 95 de la variaciones
x = principalDf['principal component 1']
y = principalDf['principal component 2']
plt.scatter(x,y, s=20 , cmap ='BrBG')
      
plt.xlabel('Componente Principal 1')
plt.ylabel('Componente Principal 2')
plt.legend(loc='lower center')
plt.tight_layout()
plt.show()

No handles with labels found to put in legend.
In [113]:
# aplicamos clusteri k-means con K=3
from sklearn.cluster import KMeans

 
#Initialize the class object
kmeans = KMeans(n_clusters= 3)
 
#predict the labels of clusters.
label = kmeans.fit_predict(principalDf)

print (label )

[2 2 2 ... 0 0 1]
In [114]:
type(label)
Out[114]:
numpy.ndarray
In [115]:
#Este código , muestra la distribución ó cantidad de puntos por acumulado ej. 01,02,03
import matplotlib.pyplot as plt
 
#filter rows of original data
filtered_label0 = principalDf[label == 0]
filtered_label1 = principalDf[label == 1]
filtered_label2 = principalDf[label == 2]
#filtered_label3 = principalDf[label == 3]


#plotting the results
#plt.scatter(filtered_label0[:,0] , filtered_label0[:,1])
plt.scatter(filtered_label0['principal component 1'] , filtered_label0['principal component 2'])
plt.scatter(filtered_label1['principal component 1'] , filtered_label1['principal component 2'])
plt.scatter(filtered_label2['principal component 1'] , filtered_label2['principal component 2'])
#plt.scatter(filtered_label3['principal component 1'] , filtered_label3['principal component 2'])

plt.show()
In [81]:
#filter rows of original data
import matplotlib
matplotlib.use('Agg')
filtered_label1 = principalDf[label == 1]
filtered_label2 = principalDf[label == 2]
filtered_label3 = principalDf[label == 3]


 
#Plotting the results
#plt.scatter(filtered_label0['principal component 1'] , filtered_label0['principal component 2'] , color = 'blue')
#plt.scatter(filtered_label1['principal component 1'] , filtered_label1['principal component 2'] , color = 'white')
#plt.scatter(filtered_label2['principal component 1'] , filtered_label2['principal component 2'] , color = 'sky')
plt.scatter(filtered_label0['principal component 1'] , filtered_label0['principal component 2'] , color = 'lightblue')
plt.scatter(filtered_label1['principal component 1'] , filtered_label1['principal component 2'] , color = 'azure')
plt.scatter(filtered_label2['principal component 1'] , filtered_label2['principal component 2'] , color=   'gray')

#plt.scatter(filtered_label3['principal component 1'] , filtered_label3['principal component 2'] , color = 'yellow')

#Getting the Centroids
centroids = kmeans.cluster_centers_
plt.scatter(centroids[:,0] , centroids[:,1] , s = 80, color = 'black')
plt.legend()
plt.show()

No handles with labels found to put in legend.
<ipython-input-81-13576da47465>:24: UserWarning: Matplotlib is currently using agg, which is a non-GUI backend, so cannot show the figure.
  plt.show()
In [116]:
print ("centros  : ",kmeans.cluster_centers_ )

centros  :  [[-0.14745587 -0.23163063]
 [ 0.78729245  0.07118533]
 [-0.21764594  0.35969314]]
In [117]:
principalDf
Out[117]:
principal component 1 principal component 2
0 -0.218739 0.588618
1 -0.220690 0.581294
2 -0.466054 0.530852
3 -0.223739 0.572324
4 -0.283288 0.559777
... ... ...
1027 0.775016 -0.183960
1028 -0.000424 -0.369749
1029 0.137103 -0.329145
1030 -0.007052 -0.367563
1031 0.606193 -0.203419

1032 rows × 2 columns

In [118]:
principalDf['y']= label
principalDf
Out[118]:
principal component 1 principal component 2 y
0 -0.218739 0.588618 2
1 -0.220690 0.581294 2
2 -0.466054 0.530852 2
3 -0.223739 0.572324 2
4 -0.283288 0.559777 2
... ... ... ...
1027 0.775016 -0.183960 1
1028 -0.000424 -0.369749 0
1029 0.137103 -0.329145 0
1030 -0.007052 -0.367563 0
1031 0.606193 -0.203419 1

1032 rows × 3 columns

In [119]:
# Fitting Logistic Regression To the training set
from sklearn.linear_model import LogisticRegression 
from sklearn.model_selection import train_test_split
from matplotlib.colors import ListedColormap




 
X_train, X_test, y_train, y_test = train_test_split(principalDf, label, test_size = 0.2, random_state = 0)

pca = PCA(n_components =2)
 
X_train = pca.fit_transform(X_train)
X_test = pca.transform(X_test)
 

classifier = LogisticRegression(random_state = 0)
classifier.fit(X_train, y_train)
Out[119]:
LogisticRegression(random_state=0)
In [120]:
# Predicting the training set
# result through scatter plot
from matplotlib.colors import ListedColormap

X_set, y_set = X_train, y_train
X1, X2 = np.meshgrid(np.arange(start = X_set[:, 0].min() - 1,
                    stop = X_set[:, 0].max() + 1, step = 0.01),
                    np.arange(start = X_set[:, 1].min() - 1,
                    stop = X_set[:, 1].max() + 1, step = 0.01))

plt.contourf(X1, X2, classifier.predict(np.array([X1.ravel(),
            X2.ravel()]).T).reshape(X1.shape), alpha = 0.75,
            cmap = ListedColormap(('gray', 'azure', 'lightblue')))
#             cmap = ListedColormap(('gray', 'white')))

plt.xlim(X1.min(), X1.max())
plt.ylim(X2.min(), X2.max())

for i, j in enumerate(np.unique(y_set)):
    plt.scatter(X_set[y_set == j, 0], X_set[y_set == j, 1],
                 c = ListedColormap(('white', 'black', 'gray'))(i), label = j)
#                 c = ListedColormap(('white', 'black', ))(i), label = j)


plt.title('Logistic Regression (Training set)')
plt.xlabel('PC1') # for Xlabel
plt.ylabel('PC2') # for Ylabel
plt.legend() # to show legend

# show scatter plot
plt.show()

*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.
*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.
*c* argument looks like a single numeric RGB or RGBA sequence, which should be avoided as value-mapping will have precedence in case its length matches with *x* & *y*.  Please use the *color* keyword-argument or provide a 2-D array with a single row if you intend to specify the same RGB or RGBA value for all points.
In [ ]:
###############################################################
#                          Finish  PCA - Analysis 
#  
###############################################################